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From Sriram <>
Subject Re: Thoughts and obesrvations on Samza
Date Thu, 02 Jul 2015 16:53:51 GMT
One thing that is worth exploring is to have a transformation and ingestion library in Kafka
but use the same framework for fault tolerance, resource isolation and management. The biggest
difference I see in these two use cases is the API and data model.

> On Jul 2, 2015, at 8:59 AM, Jay Kreps <> wrote:
> Hey Garry,
> Yeah that's super frustrating. I'd be happy to chat more about this if
> you'd be interested. I think Chris and I started with the idea of "what
> would it take to make Samza a kick-ass ingestion tool" but ultimately we
> kind of came around to the idea that ingestion and transformation had
> pretty different needs and coupling the two made things hard.
> For what it's worth I think copycat (KIP-26) actually will do what you are
> looking for.
> With regard to your point about slider, I don't necessarily disagree. But I
> think getting good YARN support is quite doable and I think we can make
> that work well. I think the issue this proposal solves is that technically
> it is pretty hard to support multiple cluster management systems the way
> things are now, you need to write an "app master" or "framework" for each
> and they are all a little different so testing is really hard. In the
> absence of this we have been stuck with just YARN which has fantastic
> penetration in the Hadoopy part of the org, but zero penetration elsewhere.
> Given the huge amount of work being put in to slider, marathon, aws
> tooling, not to mention the umpteen related packaging technologies people
> want to use (Docker, Kubernetes, various cloud-specific deploy tools, etc)
> I really think it is important to get this right.
> -Jay
> On Thu, Jul 2, 2015 at 4:17 AM, Garry Turkington <
>> wrote:
>> Hi all,
>> I think the question below re does Samza become a sub-project of Kafka
>> highlights the broader point around migration. Chris mentions Samza's
>> maturity is heading towards a v1 release but I'm not sure it feels right to
>> launch a v1 then immediately plan to deprecate most of it.
>> From a selfish perspective I have some guys who have started working with
>> Samza and building some new consumers/producers was next up. Sounds like
>> that is absolutely not the direction to go. I need to look into the KIP in
>> more detail but for me the attractiveness of adding new Samza
>> consumer/producers -- even if yes all they were doing was really getting
>> data into and out of Kafka --  was to avoid  having to worry about the
>> lifecycle management of external clients. If there is a generic Kafka
>> ingress/egress layer that I can plug a new connector into and have a lot of
>> the heavy lifting re scale and reliability done for me then it gives me all
>> the pushing new consumers/producers would. If not then it complicates my
>> operational deployments.
>> Which is similar to my other question with the proposal -- if we build a
>> fully available/stand-alone Samza plus the requisite shims to integrate
>> with Slider etc I suspect the former may be a lot more work than we think.
>> We may make it much easier for a newcomer to get something running but
>> having them step up and get a reliable production deployment may still
>> dominate mailing list  traffic, if for different reasons than today.
>> Don't get me wrong -- I'm comfortable with making the Samza dependency on
>> Kafka much more explicit and I absolutely see the benefits  in the
>> reduction of duplication and clashing terminologies/abstractions that
>> Chris/Jay describe. Samza as a library would likely be a very nice tool to
>> add to the Kafka ecosystem. I just have the concerns above re the
>> operational side.
>> Garry
>> -----Original Message-----
>> From: Gianmarco De Francisci Morales []
>> Sent: 02 July 2015 12:56
>> To:
>> Subject: Re: Thoughts and obesrvations on Samza
>> Very interesting thoughts.
>> From outside, I have always perceived Samza as a computing layer over
>> Kafka.
>> The question, maybe a bit provocative, is "should Samza be a sub-project
>> of Kafka then?"
>> Or does it make sense to keep it as a separate project with a separate
>> governance?
>> Cheers,
>> --
>> Gianmarco
>>> On 2 July 2015 at 08:59, Yan Fang <> wrote:
>>> Overall, I agree to couple with Kafka more tightly. Because Samza de
>>> facto is based on Kafka, and it should leverage what Kafka has. At the
>>> same time, Kafka does not need to reinvent what Samza already has. I
>>> also like the idea of separating the ingestion and transformation.
>>> But it is a little difficult for me to image how the Samza will look
>> like.
>>> And I feel Chris and Jay have a little difference in terms of how
>>> Samza should look like.
>>> *** Will it look like what Jay's code shows (A client of Kakfa) ? And
>>> user's application code calls this client?
>>> 1. If we make Samza be a library of Kafka (like what the code shows),
>>> how do we implement auto-balance and fault-tolerance? Are they taken
>>> care by the Kafka broker or other mechanism, such as "Samza worker"
>>> (just make up the name) ?
>>> 2. What about other features, such as auto-scaling, shared state,
>>> monitoring?
>>> *** If we have Samza standalone, (is this what Chris suggests?)
>>> 1. we still need to ingest data from Kakfa and produce to it. Then it
>>> becomes the same as what Samza looks like now, except it does not rely
>>> on Yarn anymore.
>>> 2. if it is standalone, how can it leverage Kafka's metrics, logs,
>>> etc? Use Kafka code as the dependency?
>>> Thanks,
>>> Fang, Yan
>>>> On Wed, Jul 1, 2015 at 5:46 PM, Guozhang Wang <>
>>> wrote:
>>>> Read through the code example and it looks good to me. A few
>>>> thoughts regarding deployment:
>>>> Today Samza deploys as executable runnable like:
>>>> deploy/samza/bin/ --config-factory=...
>> --config-path=file://...
>>>> And this proposal advocate for deploying Samza more as embedded
>>>> libraries in user application code (ignoring the terminology since
>>>> it is not the
>>> same
>>>> as the prototype code):
>>>> StreamTask task = new MyStreamTask(configs); Thread thread = new
>>>> Thread(task); thread.start();
>>>> I think both of these deployment modes are important for different
>>>> types
>>> of
>>>> users. That said, I think making Samza purely standalone is still
>>>> sufficient for either runnable or library modes.
>>>> Guozhang
>>>>> On Tue, Jun 30, 2015 at 11:33 PM, Jay Kreps <>
>>>>> Looks like gmail mangled the code example, it was supposed to look
>>>>> like
>>>>> this:
>>>>> Properties props = new Properties();
>>>>> props.put("bootstrap.servers", "localhost:4242"); StreamingConfig
>>>>> config = new StreamingConfig(props);
>>>>> config.subscribe("test-topic-1", "test-topic-2");
>>>>> config.processor(ExampleStreamProcessor.class);
>>>>> config.serialization(new StringSerializer(), new
>>>>> StringDeserializer()); KafkaStreaming container = new
>>>>> KafkaStreaming(config);;
>>>>> -Jay
>>>>> On Tue, Jun 30, 2015 at 11:32 PM, Jay Kreps <>
>> wrote:
>>>>>> Hey guys,
>>>>>> This came out of some conversations Chris and I were having
>>>>>> around
>>>>> whether
>>>>>> it would make sense to use Samza as a kind of data ingestion
>>> framework
>>>>> for
>>>>>> Kafka (which ultimately lead to KIP-26 "copycat"). This kind of
>>>> combined
>>>>>> with complaints around config and YARN and the discussion around
>>>>>> how
>>> to
>>>>>> best do a standalone mode.
>>>>>> So the thought experiment was, given that Samza was basically
>>>>>> already totally Kafka specific, what if you just embraced that
>>>>>> and turned it
>>>> into
>>>>>> something less like a heavyweight framework and more like a
>>>>>> third
>>> Kafka
>>>>>> client--a kind of "producing consumer" with state management
>>>> facilities.
>>>>>> Basically a library. Instead of a complex stream processing
>>>>>> framework
>>>>> this
>>>>>> would actually be a very simple thing, not much more complicated
>>>>>> to
>>> use
>>>>> or
>>>>>> operate than a Kafka consumer. As Chris said we thought about it
>>>>>> a
>>> lot
>>>> of
>>>>>> what Samza (and the other stream processing systems were doing)
>>> seemed
>>>>> like
>>>>>> kind of a hangover from MapReduce.
>>>>>> Of course you need to ingest/output data to and from the stream
>>>>>> processing. But when we actually looked into how that would
>>>>>> work,
>>> Samza
>>>>>> isn't really an ideal data ingestion framework for a bunch of
>>> reasons.
>>>> To
>>>>>> really do that right you need a pretty different internal data
>>>>>> model
>>>> and
>>>>>> set of apis. So what if you split them and had an api for Kafka
>>>>>> ingress/egress (copycat AKA KIP-26) and a separate api for Kafka
>>>>>> transformation (Samza).
>>>>>> This would also allow really embracing the same terminology and
>>>>>> conventions. One complaint about the current state is that the
>>>>>> two
>>>>> systems
>>>>>> kind of feel bolted on. Terminology like "stream" vs "topic" and
>>>>> different
>>>>>> config and monitoring systems means you kind of have to learn
>>>>>> Kafka's
>>>>> way,
>>>>>> then learn Samza's slightly different way, then kind of
>>>>>> understand
>>> how
>>>>> they
>>>>>> map to each other, which having walked a few people through this
>>>>>> is surprisingly tricky for folks to get.
>>>>>> Since I have been spending a lot of time on airplanes I hacked
>>>>>> up an ernest but still somewhat incomplete prototype of what
>>>>>> this would
>>> look
>>>>>> like. This is just unceremoniously dumped into Kafka as it
>>>>>> required a
>>>> few
>>>>>> changes to the new consumer. Here is the code:
>>> /apache/kafka/clients/streaming
>>>>>> For the purpose of the prototype I just liberally renamed
>>>>>> everything
>>> to
>>>>>> try to align it with Kafka with no regard for compatibility.
>>>>>> To use this would be something like this:
>>>>>> Properties props = new Properties();
>>>>>> props.put("bootstrap.servers", "localhost:4242");
>>>>>> StreamingConfig config = new
>>> StreamingConfig(props);
>>>>> config.subscribe("test-topic-1",
>>>>>> "test-topic-2"); config.processor(ExampleStreamProcessor.class);
>>>>> config.serialization(new
>>>>>> StringSerializer(), new StringDeserializer()); KafkaStreaming
>>>> container =
>>>>>> new KafkaStreaming(config);;
>>>>>> KafkaStreaming is basically the SamzaContainer; StreamProcessor
>>>>>> is basically StreamTask.
>>>>>> So rather than putting all the class names in a file and then
>>>>>> having
>>>> the
>>>>>> job assembled by reflection, you just instantiate the container
>>>>>> programmatically. Work is balanced over however many instances
>>>>>> of
>>> this
>>>>> are
>>>>>> alive at any time (i.e. if an instance dies, new tasks are added
>>>>>> to
>>> the
>>>>>> existing containers without shutting them down).
>>>>>> We would provide some glue for running this stuff in YARN via
>>>>>> Slider, Mesos via Marathon, and AWS using some of their tools
>>>>>> but from the
>>>> point
>>>>> of
>>>>>> view of these frameworks these stream processing jobs are just
>>>> stateless
>>>>>> services that can come and go and expand and contract at will.
>>>>>> There
>>> is
>>>>> no
>>>>>> more custom scheduler.
>>>>>> Here are some relevant details:
>>>>>>   1. It is only ~1300 lines of code, it would get larger if we
>>>>>>   productionized but not vastly larger. We really do get a ton
>>>>>> of
>>>>> leverage
>>>>>>   out of Kafka.
>>>>>>   2. Partition management is fully delegated to the new consumer.
>>> This
>>>>>>   is nice since now any partition management strategy available
>>>>>> to
>>>> Kafka
>>>>>>   consumer is also available to Samza (and vice versa) and with
>>>>>> the
>>>>> exact
>>>>>>   same configs.
>>>>>>   3. It supports state as well as state reuse
>>>>>> Anyhow take a look, hopefully it is thought provoking.
>>>>>> -Jay
>>>>>> On Tue, Jun 30, 2015 at 6:55 PM, Chris Riccomini <
>>>>>> wrote:
>>>>>>> Hey all,
>>>>>>> I have had some discussions with Samza engineers at LinkedIn
>>>>>>> and
>>>>> Confluent
>>>>>>> and we came up with a few observations and would like to
>>>>>>> propose
>>> some
>>>>>>> changes.
>>>>>>> We've observed some things that I want to call out about
>>>>>>> Samza's
>>>> design,
>>>>>>> and I'd like to propose some changes.
>>>>>>> * Samza is dependent upon a dynamic deployment system.
>>>>>>> * Samza is too pluggable.
>>>>>>> * Samza's SystemConsumer/SystemProducer and Kafka's consumer
>>>>>>> APIs
>>> are
>>>>>>> trying to solve a lot of the same problems.
>>>>>>> All three of these issues are related, but I'll address them
>>> order.
>>>>>>> Deployment
>>>>>>> Samza strongly depends on the use of a dynamic deployment
>>>>>>> scheduler
>>>> such
>>>>>>> as
>>>>>>> YARN, Mesos, etc. When we initially built Samza, we bet that
>>>>>>> there
>>>> would
>>>>>>> be
>>>>>>> one or two winners in this area, and we could support them, and
>>>>>>> the
>>>> rest
>>>>>>> would go away. In reality, there are many variations.
>>>>>>> Furthermore,
>>>> many
>>>>>>> people still prefer to just start their processors like normal
>>>>>>> Java processes, and use traditional deployment scripts such as
>>>>>>> Fabric,
>>>> Chef,
>>>>>>> Ansible, etc. Forcing a deployment system on users makes the
>>>>>>> Samza start-up process really painful for first time users.
>>>>>>> Dynamic deployment as a requirement was also a bit of a
>>>>>>> mis-fire
>>>> because
>>>>>>> of
>>>>>>> a fundamental misunderstanding between the nature of batch jobs
>>>>>>> and
>>>>> stream
>>>>>>> processing jobs. Early on, we made conscious effort to favor
>>>>>>> the
>>>> Hadoop
>>>>>>> (Map/Reduce) way of doing things, since it worked and was well
>>>>> understood.
>>>>>>> One thing that we missed was that batch jobs have a definite
>>>> beginning,
>>>>>>> and
>>>>>>> end, and stream processing jobs don't (usually). This leads to
>>>>>>> a
>>> much
>>>>>>> simpler scheduling problem for stream processors. You basically
>>>>>>> just
>>>>> need
>>>>>>> to find a place to start the processor, and start it. The way
>>>>>>> we run grids, at LinkedIn, there's no concept of a cluster
>>>>>>> being "full". We always
>>>> add
>>>>>>> more machines. The problem with coupling Samza with a scheduler
>>>>>>> is
>>>> that
>>>>>>> Samza (as a framework) now has to handle deployment. This pulls
>>>>>>> in a
>>>>> bunch
>>>>>>> of things such as configuration distribution (config stream),
>>>>>>> shell
>>>>> scrips
>>>>>>> (bin/, JobRunner), packaging (all the .tgz stuff),
>>>>>>> Another reason for requiring dynamic deployment was to support
>>>>>>> data locality. If you want to have locality, you need to put
>>>>>>> your
>>>> processors
>>>>>>> close to the data they're processing. Upon further
>>>>>>> investigation,
>>>>> though,
>>>>>>> this feature is not that beneficial. There is some good
>>>>>>> discussion
>>>> about
>>>>>>> some problems with it on SAMZA-335. Again, we took the
>>>>>>> Map/Reduce
>>>> path,
>>>>>>> but
>>>>>>> there are some fundamental differences between HDFS and Kafka.
>>>>>>> HDFS
>>>> has
>>>>>>> blocks, while Kafka has partitions. This leads to less
>>>>>>> optimization potential with stream processors on top of Kafka.
>>>>>>> This feature is also used as a crutch. Samza doesn't have any
>>>>>>> built
>>> in
>>>>>>> fault-tolerance logic. Instead, it depends on the dynamic
>>>>>>> deployment scheduling system to handle restarts when a
>>>>>>> processor dies. This has
>>>>> made
>>>>>>> it very difficult to write a standalone Samza container
>> (SAMZA-516).
>>>>>>> Pluggability
>>>>>>> In some cases pluggability is good, but I think that we've gone
>>>>>>> too
>>>> far
>>>>>>> with it. Currently, Samza has:
>>>>>>> * Pluggable config.
>>>>>>> * Pluggable metrics.
>>>>>>> * Pluggable deployment systems.
>>>>>>> * Pluggable streaming systems (SystemConsumer, SystemProducer,
>> etc).
>>>>>>> * Pluggable serdes.
>>>>>>> * Pluggable storage engines.
>>>>>>> * Pluggable strategies for just about every component
>>> (MessageChooser,
>>>>>>> SystemStreamPartitionGrouper, ConfigRewriter, etc).
>>>>>>> There's probably more that I've forgotten, as well. Some of
>>>>>>> these
>>> are
>>>>>>> useful, but some have proven not to be. This all comes at a cost:
>>>>>>> complexity. This complexity is making it harder for our users
>>>>>>> to
>>> pick
>>>> up
>>>>>>> and use Samza out of the box. It also makes it difficult for
>>>>>>> Samza developers to reason about what the characteristics of
>>>>>>> the container (since the characteristics change depending on
>>>>>>> which plugins are use).
>>>>>>> The issues with pluggability are most visible in the System APIs.
>>> What
>>>>>>> Samza really requires to be functional is Kafka as its
>>>>>>> transport
>>>> layer.
>>>>>>> But
>>>>>>> we've conflated two unrelated use cases into one API:
>>>>>>> 1. Get data into/out of Kafka.
>>>>>>> 2. Process the data in Kafka.
>>>>>>> The current System API supports both of these use cases. The
>>>>>>> problem
>>>> is,
>>>>>>> we
>>>>>>> actually want different features for each use case. By papering
>>>>>>> over
>>>>> these
>>>>>>> two use cases, and providing a single API, we've introduced a
>>>>>>> ton of
>>>>> leaky
>>>>>>> abstractions.
>>>>>>> For example, what we'd really like in (2) is to have
>>>>>>> monotonically increasing longs for offsets (like Kafka). This
>>>>>>> would be at odds
>>> with
>>>>> (1),
>>>>>>> though, since different systems have different
>>>>> SCNs/Offsets/UUIDs/vectors.
>>>>>>> There was discussion both on the mailing list and the SQL JIRAs
>>> about
>>>>> the
>>>>>>> need for this.
>>>>>>> The same thing holds true for replayability. Kafka allows us
>>> rewind
>>>>>>> when
>>>>>>> we have a failure. Many other systems don't. In some cases,
>>>>>>> systems
>>>>> return
>>>>>>> null for their offsets (e.g. WikipediaSystemConsumer) because
>>>>>>> they
>>>> have
>>>>> no
>>>>>>> offsets.
>>>>>>> Partitioning is another example. Kafka supports partitioning,
>>>>>>> but
>>> many
>>>>>>> systems don't. We model this by having a single partition for
>>>>>>> those systems. Still, other systems model partitioning
>> differently (e.g.
>>>>>>> Kinesis).
>>>>>>> The SystemAdmin interface is also a mess. Creating streams in
>>>>>>> system-agnostic way is almost impossible. As is modeling
>>>>>>> metadata
>>> for
>>>>> the
>>>>>>> system (replication factor, partitions, location, etc). The
>>>>>>> list
>>> goes
>>>>> on.
>>>>>>> Duplicate work
>>>>>>> At the time that we began writing Samza, Kafka's consumer and
>>> producer
>>>>>>> APIs
>>>>>>> had a relatively weak feature set. On the consumer-side, you
>>>>>>> had two
>>>>>>> options: use the high level consumer, or the simple consumer.
>>>>>>> The
>>>>> problem
>>>>>>> with the high-level consumer was that it controlled your
>>>>>>> offsets, partition assignments, and the order in which you
>>>>>>> received messages. The
>>> problem
>>>>>>> with
>>>>>>> the simple consumer is that it's not simple. It's basic. You
>>>>>>> end up
>>>>> having
>>>>>>> to handle a lot of really low-level stuff that you shouldn't.
>>>>>>> We
>>>> spent a
>>>>>>> lot of time to make Samza's KafkaSystemConsumer very robust.
>>>>>>> also allows us to support some cool features:
>>>>>>> * Per-partition message ordering and prioritization.
>>>>>>> * Tight control over partition assignment to support joins,
>>>>>>> global
>>>> state
>>>>>>> (if we want to implement it :)), etc.
>>>>>>> * Tight control over offset checkpointing.
>>>>>>> What we didn't realize at the time is that these features
>>>>>>> should
>>>>> actually
>>>>>>> be in Kafka. A lot of Kafka consumers (not just Samza stream
>>>> processors)
>>>>>>> end up wanting to do things like joins and partition
>>>>>>> assignment. The
>>>>> Kafka
>>>>>>> community has come to the same conclusion. They're adding a ton
>>>>>>> of upgrades into their new Kafka consumer implementation. To
>>>>>>> large extent,
>>> it's
>>>>>>> duplicate work to what we've already done in Samza.
>>>>>>> On top of this, Kafka ended up taking a very similar approach
>>>>>>> to
>>>> Samza's
>>>>>>> KafkaCheckpointManager implementation for handling offset
>>>> checkpointing.
>>>>>>> Like Samza, Kafka's new offset management feature stores offset
>>>>>>> checkpoints in a topic, and allows you to fetch them from the
>>>>>>> broker.
>>>>>>> A lot of this seems like a waste, since we could have shared
>>>>>>> the
>>> work
>>>> if
>>>>>>> it
>>>>>>> had been done in Kafka from the get-go.
>>>>>>> Vision
>>>>>>> All of this leads me to a rather radical proposal. Samza is
>>> relatively
>>>>>>> stable at this point. I'd venture to say that we're near a 1.0
>>>> release.
>>>>>>> I'd
>>>>>>> like to propose that we take what we've learned, and begin
>>>>>>> thinking
>>>>> about
>>>>>>> Samza beyond 1.0. What would we change if we were starting from
>>>> scratch?
>>>>>>> My
>>>>>>> proposal is to:
>>>>>>> 1. Make Samza standalone the *only* way to run Samza
>>>>>>> processors, and eliminate all direct dependences on YARN, Mesos,
>> etc.
>>>>>>> 2. Make a definitive call to support only Kafka as the stream
>>>> processing
>>>>>>> layer.
>>>>>>> 3. Eliminate Samza's metrics, logging, serialization, and
>>>>>>> config
>>>>> systems,
>>>>>>> and simply use Kafka's instead.
>>>>>>> This would fix all of the issues that I outlined above. It
>>>>>>> should
>>> also
>>>>>>> shrink the Samza code base pretty dramatically. Supporting only
>>>>>>> a standalone container will allow Samza to be executed on YARN
>>>>>>> (using Slider), Mesos (using Marathon/Aurora), or most other
>>>>>>> in-house
>>>>> deployment
>>>>>>> systems. This should make life a lot easier for new users.
>>>>>>> Imagine
>>>>> having
>>>>>>> the hello-samza tutorial without YARN. The drop in mailing list
>>>> traffic
>>>>>>> will be pretty dramatic.
>>>>>>> Coupling with Kafka seems long overdue to me. The reality is,
>>> everyone
>>>>>>> that
>>>>>>> I'm aware of is using Samza with Kafka. We basically require
>>>> already
>>>>> in
>>>>>>> order for most features to work. Those that are using other
>>>>>>> systems
>>>> are
>>>>>>> generally using it for ingest into Kafka (1), and then they do
>>>>>>> the processing on top. There is already discussion (
>>> 767
>>>>>>> )
>>>>>>> in Kafka to make ingesting into Kafka extremely easy.
>>>>>>> Once we make the call to couple with Kafka, we can leverage a
>>>>>>> ton of
>>>>> their
>>>>>>> ecosystem. We no longer have to maintain our own config,
>>>>>>> metrics,
>>> etc.
>>>>> We
>>>>>>> can all share the same libraries, and make them better. This
>>>>>>> will
>>> also
>>>>>>> allow us to share the consumer/producer APIs, and will let us
>>> leverage
>>>>>>> their offset management and partition management, rather than
>>>>>>> having
>>>> our
>>>>>>> own. All of the coordinator stream code would go away, as would
>>>>>>> most
>>>> of
>>>>>>> the
>>>>>>> YARN AppMaster code. We'd probably have to push some partition
>>>>> management
>>>>>>> features into the Kafka broker, but they're already moving in
>>>>>>> that direction with the new consumer API. The features we have
>>>>>>> for
>>>> partition
>>>>>>> assignment aren't unique to Samza, and seem like they should
>>>>>>> in
>>>> Kafka
>>>>>>> anyway. There will always be some niche usages which will
>>>>>>> require
>>>> extra
>>>>>>> care and hence full control over partition assignments much
>>>>>>> like the
>>>>> Kafka
>>>>>>> low level consumer api. These would continue to be supported.
>>>>>>> These items will be good for the Samza community. They'll make
>>>>>>> Samza easier to use, and make it easier for developers to add
>>>>>>> new features.
>>>>>>> Obviously this is a fairly large (and somewhat backwards
>>> incompatible
>>>>>>> change). If we choose to go this route, it's important that we
>>> openly
>>>>>>> communicate how we're going to provide a migration path from
>>>>>>> the
>>>>> existing
>>>>>>> APIs to the new ones (if we make incompatible changes). I think
>>>>>>> at a minimum, we'd probably need to provide a wrapper to allow
>>>>>>> existing StreamTask implementations to continue running on the
>> new container.
>>>>> It's
>>>>>>> also important that we openly communicate about timing, and
>>>>>>> stages
>>> of
>>>>> the
>>>>>>> migration.
>>>>>>> If you made it this far, I'm sure you have opinions. :) Please
>>>>>>> send
>>>> your
>>>>>>> thoughts and feedback.
>>>>>>> Cheers,
>>>>>>> Chris
>>>> --
>>>> -- Guozhang

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